ENCePP Guide on Methodological Standards in Pharmacoepidemiology

5.3.4. Propensity scores

Databases used in
pharmacoepidemiological studies often include records of prescribed medications
and encounters with medical care providers, from which one can construct
surrogate measures for both drug exposure and covariates that are potential
confounders. It is often possible to track day-by-day changes in these
variables. However, while this information can be critical for study success,
its volume can pose challenges for statistical analysis.

A propensity score (PS) is
analogous to the disease risk score in that it combines a large number of
possible confounders into a single variable (the score). The exposure propensity
score (EPS) is the conditional probability of exposure to a treatment given
observed covariates. In a cohort study, matching or stratifying treated and
comparison subjects on EPS tends to balance all of the observed covariates.
However, unlike random assignment of treatments, the propensity score may not
balance unobserved covariates. Invited Commentary: Propensity Scores (Am J Epidemiol
1999;150:327–33) reviews the uses and limitations of propensity scores and
provide a brief outline of the associated statistical theory. The authors
present results of adjustment by matching or stratification on the propensity
score.

Performance of propensity score calibration – a
simulation study (Am J Epidemiol
2007;165(10):1110-8) introduces ‘propensity score calibration’ (PSC). This
technique combines propensity score matching methods with measurement error
regression models to address confounding by variables unobserved in the main
study. This is done by using additional covariate measurements observed in a
validation study, which is often a subset of the main study.

Although in most situations
propensity score models, with the exception of hd-PS, do not have any advantages
over conventional multivariate modelling in terms of adjustment for identified
confounders, several other benefits may be derived. Propensity score methods may
help to gain insight into determinants of treatment including age, frailty and
comorbidity and to identify individuals treated against expectation. A
statistical advantage of PS analyses is that if exposure is not infrequent it is
possible to adjust for a large number of covariates even if outcomes are rare, a
situation often encountered in drug safety research. Furthermore, assessment of
the PS distribution may reveal non-positivity. An important limitation of PS is
that it is not directly amenable for case-control studies.